An “AI misdiagnosis” problem isn’t usually about a chatbot “making the diagnosis.” More often, the concern is that automated tools influenced parts of the process—sometimes subtly.
In Selma, diagnostic errors can surface in familiar settings:
- Emergency and urgent care visits where triage and initial impressions shape what gets ordered next
- Hospital workflows where imaging, lab flags, or risk scores affect what clinicians notice (and what they assume)
- System-generated documentation where information is summarized incorrectly or incompletely
- Follow-up delays when abnormal results aren’t escalated the way they should be
Legally, the key question is whether the care team acted reasonably with the information available at the time. If a tool’s recommendation was treated as definitive, if safeguards weren’t used, or if conflicts with objective findings weren’t resolved, that can matter.


